Factors Affecting Online Purchase Intention: Effects of Technology and Social Commerce
Bibliographic record
Abstract
With the rapid development of information communication technologies and enhanced Internet penetration, the nature of a consumer’s daily activities has changed and most offline activities have migrated towards online activities. Moreover, customers have shown a greater tendency to shift to online activities from their traditional offline activities. In this light, e-commerce transactions in Sri Lanka are expected to grow in the near future. Apart from traditional Internet technologies, a variety of new social commerce activities has started influencing the behavior of customer activities including the online purchasing. Even though the impact of traditional Internet technologies on purchase intention of customers has been examined by many researchers, the same has not been examined adequately in relation to social commerce related activities. Therefore, this study is aimed at identifying the factors affecting online purchase intention of customers from both the technological and social commerce perspective. The theoretical model developed in the study was empirically tested through survey of 292 MBA students from two leading universities and a prominent institute in Sri Lanka. Structural Equation Modeling (SEM) was used to analyze the data. The study revealed that online purchase intention positively and significantly related with perceived usefulness, perceived ease of use, website content and trust. Moreover, it was identified that trust has a full mediation effect between perceived ease of use and purchase intention as well as between website content and purchase intention. Further, it was found that trust has a partial mediation between perceived usefulness and purchase intention.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".